Six Sigma Basics - MIT OpenCourseWare

1 Six Sigma Basics Six Sigma Basics - Slide 2 2012 Massachusetts Institute of Technology Learning Objectives At the end of this module, you will be able to: Recognize that Six Sigma is a valuable approach for improving process quality Interpret a basic Statistical Process Control chart Distinguish between process and specified control limits Describe a capable process Six Sigma Basics - Slide 3 2012 Massachusetts Institute of Technology What is Six Sigma ? A Strategy to improve process quality by identifying and eliminating defects and minimizing variation in process outputs A data driven approach based on Measurement of the process variation using Statistical Process Control A structured Implementation approach based on a DMAIC cycle and certified experts The goal of Six Sigma is to reduce process variation Six Sigma Basics - Slide 4 2012 Massachusetts Institute of Technology Standard Normal Distribution Curve Some notable qualities of the normal distribution: The mean is also its mode and median.

2 Of the area (green) is within one standard deviation of the mean. of the area (green & yellow) is within two standard deviations. of the area (green & yellow & red) is within three standard deviations 1 2 3 Sigma ( ) = one standard deviation Six Sigma Basics - Slide 5 2012 Massachusetts Institute of Technology Defects Defect is defined as any process output that does not meet the customer s specifications. Improving quality means reducing the defects per million opportunities (DPMO). There are two attributes to this metric that can be controlled: Opportunities reducing the number of steps, handoffs and other opportunities will help improve quality Defects reducing the number of defects for each process step through continuous process improvement will help improve quality Six Sigma Basics - Slide 6 2012 Massachusetts Institute of Technology Six Sigma Practical Meaning 20,000 lost articles of mail per hour Unsafe drinking water for almost 15 minutes per day 5,000 incorrect surgical operations per week Two short or long landings at most major airports each day 200.

3 000 wrong drug prescriptions each year No electricity for almost seven hours each month Seven articles of mail lost per hour One unsafe minute every seven months incorrect operations per week One short or long landing every five years 68 wrong prescriptions each year One hour without electricity every 34 years 99% GOOD ( Sigma ) GOOD (6 Sigma ) Six Sigma Basics - Slide 7 2012 Massachusetts Institute of Technology Statistical Process Control Control charting is the primary tool of SPC Control charts provide information about the stability/predictability of the process, specifically with regard to its: Central tendency (to target value) Variation SPC charts are time-sequence charts of important process or product characteristics Six Sigma Basics - Slide 8 2012 Massachusetts Institute of Technology Class Exercise Pharmacy wants to monitor the dispensing of doses of White Bean Medicine A 3 cup sample will be taken each day and weighed and recorded on a check sheet Data will be entered into two control charts (one for means or averages and one for range) Data for the first twenty days will establish the current process capability From then on, the pharmacy will monitor the dosages by entering daily samples into the control chart Process improvements will be made as needed, based upon data collected.

4 Six Sigma Basics - Slide 9 2012 Massachusetts Institute of Technology What To Do Phase I Process Capability Select three cups with the same sample number (day) Weigh each on the digital scale Record the data on the check sheet form and calculate the mean (average) and report the results to the instructor Also report the lowest and highest weights for each day. Calculate range = highest - lowest Six Sigma Basics - Slide 10 2012 Massachusetts Institute of Technology Six Sigma Process - DMAIC Define Who are the customers and what are their requirements Identify key characteristics important to the customer Measure Categorize key input and output characteristics, verify measurement systems Collect data and establish the baseline performance Analyze Convert raw data into information to provide insights into the process Improve Develop solutions to improve process capability and compare the results to the baseline performance Control Monitor the process to assure no unexpected changes occur Six Sigma Basics - Slide 11 2012 Massachusetts Institute of Technology Simple DMAIC Example DMAIC is easy to see in process control applications The same steps can be used to analyze more complex systems.

5 Often in tandem with lean tools Process Input Output Define Improve Improved Process Control Analyze Measure Measurement System Six Sigma Basics - Slide 12 2012 Massachusetts Institute of Technology Types of Process Variation Common Cause Variation is the sum of many chances causes, none traceable to a single major cause. Common cause variation is essentially the noise in the system. When a process is operating subject to common cause variation it is in a state of statistical control. Special Cause Variation is due to differences between people, machines, materials, methods, etc. The occurrence of a special (or assignable) cause results in an out of control condition. Control charts provide a means for distinguishing between common cause variability and special cause variability Six Sigma Basics - Slide 13 2012 Massachusetts Institute of Technology Control Chart Example - Patient Falls Reference: National Quality Measures Clearing House UCL LCL Phase I Establish Process Capability Phase II Monitor the Process Six Sigma Basics - Slide 14 2012 Massachusetts Institute of Technology What To Do Phase II Process Monitoring Draw control limits on your chart based on the first 20 samples.

6 Weigh a new sample (3 cups), record the data on the 2nd check sheet and calculate the average and range. Plot the average and range on the charts, and decide if the process is in control. If the process goes out of control, stop and investigate the cause using a fishbone diagram. UCL LCL Six Sigma Basics - Slide 15 2012 Massachusetts Institute of Technology Control Chart Example - c Chart for Resident Falls Source: Faten Mitchell, Quality Improvement Advisor, Health Quality Ontario Courtesy of Faten Mitchell, Quality Improvement Advisor, Health Quality Ontario. Used with Sigma Basics - Slide 16 2012 Massachusetts Institute of Technology Process Improvement and Control Charts - Starting Process Input Output In early stages, control charts (usually on output variables) are used to understand the behavior of the process After corrective actions, place charts on critical input variables Measurement System Six Sigma Basics - Slide 17 2012 Massachusetts Institute of Technology Process Improvement and Control Charts - Sustaining The goal.

7 Monitor and control inputs and, over time, eliminate the need for SPC charts by having preventative measures in place If a chart has been implemented, remove it if it is not providing valuable and actionable information Process Input Output Measurement System Six Sigma Basics - Slide 18 2012 Massachusetts Institute of Technology Process Capability Process Capability is broadly defined as the ability of a process to meet customer expectations (Bothe, 1997) Once we have a process in control then we can answer the question of whether the process is capable of meeting the customer s specifications. Six Sigma Basics - Slide 19 2012 Massachusetts Institute of Technology Customer and Process Quality Defined Process Quality is a measure of the capability of a process to produce to its expected capability The upper and lower values between which the process must be controlled are known as upper and lower control limits (UCL and LCL) Customer Quality is the conformance to customer specifications within a tolerance band The upper and lower values that the customer is willing to accept are known as upper and lower specification limits (USL and LSL) How can we assure Process Capability?

8 Six Sigma Basics - Slide 20 2012 Massachusetts Institute of Technology Assessing Process Capability Cp, a term used to define process capability, is mathematically expressed by: The figure shows centered distributions with various Cp levels. Note Cps less than two have visible tails outside the acceptable limits. Bad Bad Six Sigma Basics - Slide 21 2012 Massachusetts Institute of Technology Non-Centered Distributions If the distribution is off center, the probability of a bad result drastically increases. In this case Cpk is used. It is the smaller of Very Bad or This figure shows the same distributions off-center by . The Cpks are smaller than the corresponding Cps. This illustrates the need to both control variation and accurately hit the desired mean. Center Six Sigma Basics - Slide 22 2012 Massachusetts Institute of Technology Cp versus Cpk Cp is high Cpk is low In this case, the shooter (archer) has a good eye, but all the shots are off-center Cp is high Cpk is high In this case, the shooter (archer) has a good eye, and has now adjusted the gun (bow) sight to bring the shots on target Cp is low Cpk is low In this case, the shooter (archer)

9 Has a bad eye the shots are widely dispersed and slightly off-center Six Sigma Basics - Slide 23 2012 Massachusetts Institute of Technology Implications of a Six Sigma Process Six Sigma is defined as defects per million opportunities, or a first pass yield of With a Six Sigma process even a significant shift in the process mean results in very few defects " Sigma "Process Mean , , , , , , ,672 Process Mean Shifted Six Sigma Basics - Slide 24 2012 Massachusetts Institute of Technology Wrap Up Six Sigma is an effective quality system Widely deployed in manufacturing Actively being pursued in healthcare Control charts are an effective visual aid in monitoring process capability Other SPC analysis tools are available If customer specifications for process quality (USL, LSL) can be established, Six Sigma methods can help achieve desired outcomes.

10 Six Sigma Basics - Slide 25 2012 Massachusetts Institute of Technology Reading List Bertels, T. Ed, Rath & Strong s Six Sigma Leadership handbook , John Wiley & Sons, 2003. Bothe, , Measuring Process Capability, 1997 Gitlow, and Levine, , Six Sigma for Green Belts and Champions, Foundations, DMAIC, Tools, Cases, and Certification, Prentice Hall (Pearson Education, Inc.) 2005 Harry, M. and Schroeder, R., Six Sigma , Currency Doubleday, New York, 2000 Henderson, , Six Sigma Quality Improvement with Minitab, John Wiley & Sons, 2006. Ledolter, J. and Burrill, , Statistical Quality Control, Strategies and Tools for Continual Improvement, John Wiley & Sons, Inc., 1999 Six Sigma Basics - Slide 26 2012 Massachusetts Institute of Technology Acknowledgements Contributors Dick Lewis Rolls-Royce (ret.) Jose Macedo Cal Poly, San Luis Obispo Hugh McManus Metis Design Earll Murman MIT Steve Shade Purdue University Collaborators Faten Mitchell Health Quality Ontario Jack Reismiller Rolls-Royce Six Sigma Master Black belt Julie Vannerson Indiana University School of Medicine MIT / / Introduction to Lean Six Sigma MethodsIAP 2012 For information about citing these materials or our Terms of Use, visit.